MASTER'S THESIS. In-Vehicle Prediction of Truck Driver Sleepiness

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1 MASTER'S THESIS 2007:107 CIV In-Vehicle Prediction of Truck Driver Sleepiness Lane Position Variables Kristina Mattsson Luleå University of Technology MSc Programmes in Engineering Media Technology Department of Computer Science and Electrical Engineering Division of Media Technology 2007:107 CIV - ISSN: ISRN: LTU-EX--07/107--SE

2 In-Vehicle Prediction of Truck Driver Sleepiness - Lane Position Variables - Kristina Mattsson Luleå Technical University Södertälje 2007

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4 Preface This is the final report of the master thesis project In-Vehicle Prediction of Truck Driver Sleepiness Lane Position Variables, made at Scania CV AB for Luleå Technical University (LTU) between September 2006 and February It was carried out for the department of Computer Science and Electrical Engineering at LTU with supervisor/examiner professor James P. LeBlanc. The work was conducted and carried out at Scania Technical Centre in Södertälje, in the department RCIS (cab electrical system development), with company supervisor Fredrik Ling and Maria Lundin. The project was done in collaboration with Jens Berglund from Linköping University and is a continuation of a master thesis project done by Lena Kanstrup and Maria Lundin for Scania in I would not have been able to carry out this project without the help and assistance from a number of people: Jens Berglund Fredrik Ling Maria Lundin Anders Wikman Master degree student and co-worker, Linköping University Supervisor, Scania CV AB Supervisor, Scania CV AB Group manager, RCIS, Scania CV AB James P. LeBlanc Supervisor/examiner, Luleå Technical University Magnus Lundberg Nordenvaad Examiner, Luleå Technical University Anne Bolling Albert Kircher Eva Enqvist Göran Henriksson Researcher, VTI Researcher, VTI Professor at the department of Mathematics, Linköping University Library and information services, Scania CV AB Test truck drivers, Scania CV AB Test leaders and staff at VTI I wish to thank these persons and all other enthusiastic people that have helped and encouraged me to complete this project. Thank you! Södertälje, February 2007 Kristina Mattsson 3

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6 Abstract Drivers falling asleep behind the steering wheel are the cause of many traffic accidents, and the statistics show that the number of sleepiness related accidents are escalating. Commercial drivers represent a large part of the sleepiness accident statistics, probably depending on much time spent on the road, long driving hours and the monotonous character of the roads traveled. Systems for sleepiness detection exist but the evidence to judge their applications and performance is inadequate. Sleepiness detection from cameras monitoring the driver and other driver related measures can be hard and expensive to implement. A system only using variables that could be measured from the vehicle itself, preferably using already existing sensors, would be desirable. The assignment of this master thesis project, commissioned by Scania CV AB in Södertälje, was to investigate the possibility to develop an algorithm that detects a sleepy driving behavior, using in-vehicle variables only. This project is a continuation of a previous master thesis project that investigated a patent claiming to be able to detect inattentive driving. The authors came to the conclusion that two of the variables in the patent showed promising results that should be further investigated. These were to be tried out in this project, along with other variables proved to predict driver sleepiness, by performing extensive tests. Quantitative testing, where 22 subjects drove a simulator while sleep deprived, enabled the collection of ten raw variables, measured from either the steering wheel or lane position. Examples of raw variables are steering wheel torque, yaw angle rate and lateral acceleration. These were combined in different ways to form 17 transformed variables that according to literature had shown to be correlated with a sleepy driving behavior, like the number of lane exceedances or the variance of lateral position. To be able to judge the performance of the different transformed variables, a reliable measure of the driver s actual sleepiness was needed. A subjective measure called Karolinska Sleepiness Scale (KSS) was chosen, where the drivers estimate their own sleepiness on a 1-9 scale. Each transformed variable exists in different versions depending on what limits and thresholds are used. The best version of each transformed variable was optimized compared to the KSS and forward selection with regression analysis was used to extinguish which variables should be combined to make the best formula to detect sleepiness. Since some transformed variables were not defined for all time intervals, different formulas had to be created depending on which variables that was available. This created a selection model where six different formulas were used. The algorithm performance was judged and it proved to give good results. The formulas combined in the algorithm make correct classifications, sleepy or alert driver, in more than 87 % of the cases when sleepiness threshold was set to eight, with a low false alarm rate of less than one percent. This is a promising result considering that only in-vehicle variables were used. A better performance would probably come from combining the detection from in-vehicle variables with another sleepiness measure. The project is done in collaboration Jens Berglund from Linköping University. The work was divided during the literature study and the identification of the transformed variables, where this report focused on the lane position measurements and frequency analysis of the raw variables and Berglund (2007) addressed the steering wheel related measurers. The result and conclusion came from the combination of the steering wheel variables, lane position variables and frequency analysis variables. Keywords: Sleepiness detection, lane position, raw variables, transformed variables, truck drivers 5

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8 Table of contents 1 INTRODUCTION BACKGROUND PROBLEM ASSIGNMENT PURPOSE AND GOAL LIMITATIONS DIFFERENCE FROM EARLIER WORK PREVIOUS THESIS PROJECT DIVISION OF WORK OUTLINE OF THE REPORT 15 2 THEORETICAL FRAME OF REFERENCE SLEEPINESS DEFINITIONS SLEEPINESS FACTORS SLEEPINESS MEASURE INDEPENDENT VARIABLES - LANE POSITION MEASURES THEORY - LATERAL POSITION VARIANCE AND STANDARD DEVIATION THEORY - MEAN LATERAL POSITION THEORY - VEHICLE PATH DEVIATION THEORY - TIME-TO-LANE CROSSING THEORY - LANE EXCEEDING THEORY - FREQUENCY ANALYSIS THEORY - REACTION TIME THEORY - DEGREE OF INTERACTION INDEPENDENT VARIABLES - STEERING WHEEL MEASURES OPTIMIZATION OF TRANSFORMED VARIABLES STATISTICAL METHOD RESIDUAL SUM OF SQUARES FORWARD SELECTION F-TEST ALGORITHM APPRAISAL ALTERNATING FORMULAS 29 3 TECHNICAL FRAME OF REFERENCE SIMULATOR TECHNICAL FACTS TEST ENVIRONMENT SOFTWARE 33 4 METHOD SIMULATOR TESTING OF SLEEPY DRIVERS TEST PROCEDURE SUBJECTS RAW VARIABLES 38 7

9 4.2 IDENTIFICATION OF TRANSFORMED VARIABLES METHOD - LATERAL POSITION VARIANCE AND STANDARD DEVIATION METHOD - MEAN LATERAL POSITION METHOD - VEHICLE PATH DEVIATION METHOD - TIME-TO-LANE CROSSING METHOD - LANE EXCEEDING METHOD - FREQUENCY ANALYSIS METHOD - REACTION TIME METHOD - DEGREE OF INTERACTION OPTIMIZATION OF TRANSFORMED VARIABLES STATISTICAL ANALYSIS 42 5 RESULTS SLEEPINESS MEASURE IDENTIFIED TRANSFORMED VARIABLES RESULT - LATERAL POSITION VARIANCE AND STANDARD DEVIATION RESULT - MEAN LATERAL POSITION RESULT - VEHICLE PATH DEVIATION RESULT - TIME-TO-LANE CROSSING RESULT - LANE EXCEEDING RESULT - FREQUENCY ANALYSIS RESULT - REACTION TIME RESULT - DEGREE OF INTERACTION RESULT SUMMARY FOR TRANSFORMED VARIABLES STATISTICAL ANALYSIS AND FORMULA SELECTION ALGORITHM APPRAISAL 51 6 DISCUSSION SLEEPINESS MEASURE TRANSFORMED VARIABLES METHOD SIMULATOR TESTING STATISTICAL METHODS RESULT 56 7 CONCLUSION PROJECT CONCLUSION FUTURE PROSPECTIVE AND SYSTEM DESIGN 58 8 REFERENCES BOOKS AND COMPENDIUMS REPORTS AND PAPERS INTERNET OTHER REFERENCES 62 8

10 List of figures FIGURE 1 VEHICLE PATH DEVIATION FIGURE 2 TIME-TO-LANE CROSSING FIGURE 3 REACTION TIME FIGURE 4 DEGREE OF INTERACTION (KANSTRUP & LUNDIN, 2006) FIGURE 5 ERROR COMPUTATION WITH RESIDUAL SUM OF SQUARES FIGURE 6 MODEL FOR FORMULA SELECTOR FIGURE 7 CONTROL ROOM FOR MONITORING THE TESTS FIGURE 8 THE SIMULATOR USED IN THE EXPERIMENTS (VTI, 2006) FIGURE 9 SCHEMATIC VIEW OF THE ALGORITHM DEVELOPMENT OF THE FORMULAS FIGURE 10 EXAMPLE 3D PLOT OF THE ENERGY CONTENTS OF THE YAW RATE FIGURE 11 EXAMPLE OF TRANSFORMED VARIABLE FIGURE 12 EXAMPLE OF TRANSFORMED VARIABLE WHERE THE EXTREME VALUES HAS BEEN CUT FIGURE 13 FORMULA SELECTION FIGURE 14 NUMBER OF INTERVALS CORRESPONDING TO EACH KSS VALUE FIGURE 15 KSS VALUES FOR ALL TESTS FIGURE 16 STANDARD DEVIATION AND VARIANCE OF LATERAL POSITION FIGURE 17 POWER SPECTRAL DENSITY OF LATERAL POSITION FIGURE 18 ENERGY CONTENT IN A CERTAIN FREQUENCY BAND (0.02HZ HZ) FIGURE 19 BASELINED ENERGY CONTENT IN A CERTAIN FREQUENCY BAND (0.02HZ HZ) FIGURE 20 HISTOGRAM OF THE REACTION TIME FIGURE 21 FINAL FORMULA S ACCORDANCE TO THE KSS VALUES, THRESHOLD EIGHT FIGURE 22 FINAL FORMULA S ACCORDANCE TO THE KSS VALUES, THRESHOLD NINE List of tables TABLE 1 DIVISION OF WORK TABLE 2 KAROLINSKA SLEEPINESS SCALE TABLE 3 STEERING WHEEL MEASURES FOUND IN LITERATURE TABLE 4 CONTINGENCY TABLE (REVISED VERSION FROM KIRCHER ET AL., 2002) TABLE 5 RAW VARIABLES TABLE 6 VARIABLE VERSIONS TRIED IN THE FORMULAS TABLE 7 ALGORITHM APPRAISAL FOR THRESHOLD EIGHT TABLE 8 ALGORITHM APPRAISAL FOR THRESHOLD NINE List of equations EQUATION 1 DEGREE OF INTERACTION EQUATION 2 GENERAL REGRESSION STATEMENT EQUATION 3 EXAMPLE REGRESSION STATEMENT EQUATION 4 EXAMPLE RESIDUAL SUM OF SQUARES EQUATION 5 CORRELATION COEFFICIENT EQUATION 6 TEST QUANTITY FOR F-TEST

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12 1 Introduction The following introduction will provide a background to the master thesis project. The background leads to the problem with assignment, purpose, goal, limitations and difference from earlier work in the area. A summary of the preceding master thesis project that constitutes the base for this project is presented in 1.3. This project is, as mentioned, done in collaboration with Jens Berglund and 1.4 will explain how the work is divided. The closing part of the chapter is an outline for the report. 1.1 Background Sleep deprivation in drivers is suspected to cause more and more accidents on our roads. Official figures claim that driver fatigue is estimated to cause 1-3 % of the traffic accidents but this number is generally considered to be much higher. According to Åkerstedt and Kecklund (2000) the correct figures for light traffic are rather in the approximate size of % and presumably even higher for heavy traffic. There are significant concerns that statistics are inadequate since no systematic accident analysis has been carried out where accidents caused by fatigue are distinguished from those caused by other factors like alcohol or drugs. (Dinges, 1995) The National Transportation Safety Board (NTSB) in USA has also stated, in repeated reports, that fatigue related accidents constitute a much larger portion of the accidents than official figures claim. As early as 1990 they withheld that fatigue was the single most important cause to fatal accidents in heavy traffic. (NTSB, 1990) The problem of sleepy driving has existed for as long as there have been cars and trucks. In the beginning, most research resources were focused on how to diminish the effect of accidents in general and did not address prevention or detection of sleepy driving in particular. Dr David F Dinges has studied sleep deprivation, night work and the relation between fatigue and performance. He coined the phrase power napping 1 and has studied how the sleepiness of a driver affects the driving performance. ( 2005). His work has constituted the base for several other studies where the sleepiness/performance trade-off was put in the context of driving. The vehicle industry has taken an interest in this area of research and the area is now expanding. 1 A shorter nap that could help manage fatigue. 11

13 Time is nowadays expressed in 24 hour operations and more and more people are conducting vigilance-based activities at times other than the traditional daytime work hours. Starting at the end of the last century, night-time has become an opportunity for production, and there is evidence that these tendencies will escalate over time. (Dinges, 1995) The numbers of accidents caused by sleepiness is increasing due to the tendency that sleep is being less prioritized; hence more people are sleep deprived. Commercial drivers are no exception. Tight schedules and time pressure makes drivers drive for longer hours and during night time to avoid traffic. (NTSB, 1990) Individual differences on how to handle sleepiness and to which extent it affects performance exists but they are limited. There is no evidence that the need for sleep of professionals is different from what observed in other persons. Motivation, commitment and extra pay could only prevent sleepiness for transient periods of time. People in general tend to overestimate their own ability to handle their sleepiness. They often consider the risk of reduced performance less when it comes to their own driving, with the argument that precedent ensures an adequate safety margin (i.e. they have performed sleepy in the past and not had a catastrophe). Every time the citizen manages to drive sleepy without having an accident, it reinforces his or her perception of control and safety (Page 10, Dinges, 1995). The circadian rhythms 2 are low in the early morning hours as well as in the mid-afternoon. Driving during these hours could in combination with other factors like sleep loss or driving for long periods of time be the cause of sleepy driving, independent on the professionalism of the driver. (Tijerina et al., 1999) Hence, drivers are not always good judges of their own sleepiness. Even if they are aware of their sleepiness, they could be unaware of the risk of falling asleep. (Stutts et al, 2001) Populations that have been known to be at higher risk for involvement in sleep-related crashes include young people, especially young males, persons with sleeping-disorders or those who have taken soporific medications and night time or shift workers. Commercial vehicle operators are also at increased risk for sleep-related crashes due to factors like extended driving times, irregular work and sleep schedule, higher frequency of night-time driving and inadequate sleep. (Stutts et al., 2001) This opinion is also shared by Sagberg et al. (2004) who have looked at several crash statistics from different references and come to the conclusion that the problem with fatigue related crashes seems to be larger among truck drivers than drivers in general, probably due to that truck drivers mainly drive on large monotonous roads and often during night-time. The accidents associated with sleepy driving are therefore more often fatal since the speeds are higher on highways, main roads and motorways combined with a delayed reaction time of the driver. Dinges (1995) claims that statistics, although inadequate, indicates that the sleepiness-related accidents are common on long stretches of motorway, perhaps accounting for 40 % or more of the fatal crashes. According to him, NTSB (1990) has implicated that fatigue is the most frequent contributor to crashes in which a truck driver was fatally injured. Stutts et al. (2001) made a population-based case-control study where drivers involved in accidents in North Carolina were interviewed over the telephone. They compared a group of drivers that had been reported (by the police) to be asleep or fatigued by the time of the crash with a control group where drivers had either been in a recent crash not related to fatigue or not in a crash at all. Results showed that drivers in the sleep-related crashes were more likely to work multiple jobs, night shifts, or other irregular work schedules, more likely to have used soporific medications, had been driving for longer time and had slept fewer hours the night before. They also reported poorer quality of sleep (and averaged less sleep per night), drove more often late at night, and had more prior instances of sleepy driving. Dinges (1995) states that a low level of vigilance gives risk to a dangerous driving style in terms of steering wheel movements, lane keeping and speed variation. Referenced in Kanstrup & Lundin (2006) are Vincent et al. (1998) who studied changes in steering wheel activity associated with decreased alertness. Their study showed that small magnitude steering wheel movements decreased in frequency when the driver was getting sleepier, while large magnitude steering wheel movements increased in frequency. Also referenced in Kanstrup & Lundin (2006) are Haworth & Heffernan (1989) showing possible warning signs of a sleepy driver: no memory of the last few miles driven zigzag driving, lane drifting, hitting rumble strips, keeps jerking the vehicle back into the lane wandering or disconnected thoughts repeated yawns difficulty to keep eyes open tailgating 3 2 Circadian rhythm means an innate, daily, fluctuation of behavioral and physiological functions, which include sleeping and waking. (Sleep Terms, Definitions and Abbreviations, in Kanstrup & Lundin, 2006) 3 American expression for driving too close to the vehicle in front of you. 12

14 misses traffic lights has trouble keeping head up Regulations in the Swedish law permit the driver to drive for four and a half hours before taking a brake of at least 45 minutes. These rules are the same in the European Union and apply to vehicles registered in EU that weighs over 3500 kg, buses included. The driver can not drive for more than nine hours a day, although this can be extended to ten hours a day two times a week. After driving six days in a row the driver has to have a weekrest of normally 45 hours. The driver is not allowed to drive more than 90 hours in a two-week period. (Vägverket, 2006) All of these rules aim to prevent sleepy driving and similar regulations exist in most countries. The economic needs of the industrialized countries of America and Europe are changing. Also, the scientific data of the consequences of sleepy driving has accumulated during the last 30 years. This combined with the publics perception of what should be regulated constitutes the base for the revision of regulations in many countries. Dinges (1995) warns that governments need to take these factors in to account when changing regulations or they will be doomed to be ineffective in the prevention of fatigue-related accidents. Governments try to approach the problem of sleepy driving by tightening regulations and improving or changing road design. One such road design change is the installation of rumble strips 4 along the shoulder of selected roadways to alert drifting drivers, another is to diminish the outcome of the accident by building fences or roads with separated lanes to avoid oncoming traffic. Also, educating the public about the risks of sleepiness when driving may reduce the number of sleepy drivers. There are also biobehavioral countermeasures to fatigue like application of preplanned naps, caffeine consumption and bright light. (Dinges, 1995) Airbags and seat-belts to protect passengers in the event of an accident are also a kind of secondary prevention that traditionally has been the focus of safety development in the vehicle. Nowadays the vehicle industry investigates other countermeasures where the aim is to predict sleepiness and prevent the driver from falling asleep by monitoring the driver and give some kind of warning when sleepiness is detected. Other systems aiming to assist the driver in the vehicle are called ADAS (Advanced Driver Assistance Systems) and include parking aid, night vision, lane departure warning systems and adaptive cruise control. Some of these systems are implemented and others will assumingly be implemented in trucks in the near future. (Kanstrup & Lundin, 2006) For example, Scania CV AB has introduced the LDWS (Lane Departure Warning System), which is meant to hinder the driver from making a non deliberate lane departure. In-vehicle sleepiness detection measures uses variables from the vehicle itself. These systems are based on the idea that a driver goes through different stages as he/she is getting sleepier. Before the sleepy stage there is usually a period of degraded driving which can be detected by measuring different in-vehicle variables. (Knipling & Wierwille, 1994) Such a system would gather signals in real-time that are then passed through an algorithm that is trained to detect a sleepy driving behavior. If the outcome from the formulas of the algorithm is higher than the set threshold, the system should produce a warning. It is important to notice the difference between detection and prediction. A system should preferably be able to predict driver sleepiness, although this is harder to achieve. Detection of driver sleepiness could be sufficient but it could also mean that the system discovers the sleepy driving behavior too late and the accident can not be avoided. 1.2 Problem Inattentive driving is a growing problem due to longer transportation and tight schedule for both private and commercial drivers. Different kinds of warning systems have been or are being developed to prevent the driver from falling asleep while driving. Some of these systems are based on body signals from the driver like EEG 5, eye movements and blinking rate. To be able to receive these signals, special sensor equipment like wiring or cameras is needed that could be obstructive or disturbing for the driver 6. Using cameras to read signals from either eye movement or blinking rate could, except from being a quite expensive method, be difficult to use in a non heterogeneous environment where different lighting and other factors like usage of glasses complicates the gathering of information. A system that measures in-vehicle signals, like steering wheel variance or lateral lane position drift variables, would be preferable. According to Dinges (1995) there are existing in-vehicle systems 4 Rumble strips are grooves or rows of raised pavement markers placed perpendicular to the direction of travel to alert inattentive drivers. ( 2007) 5 Electroencephalogram measures aims to detect brain waves typical for fatigue. 6 Renner and Mehring did research on automated sleepiness detection systems for Daimler Benz. They found that in a successful warning system it is not feasible to expect the driver to wire up with electrodes or sensors every time she/he goes on the road, hence they worked by the clear directive to never bother the driver. (Commission of the European Communities, 1998) 13

15 meant to detect driver sleepiness in commercial and noncommercial driving but the evidence to judge its application and efficiency is insufficient. In a study made of Vincent et al. (1998) where behavioral adaptation of drivers to Fatigue Warning Systems (FWS) was evaluated, they concluded that their findings suggested that FWS as currently conceived may not contribute to reduce fatigue induced collisions. This implies that systems known today do not live up to the expectations of FWS and that further studies are needed Assignment The assignment of this project, which is commissioned by Scania CV AB, is to continue where Maria Lundin and Lena Kanstrup finished off and investigate the possibilities to develop an algorithm to detect driver sleepiness, using only in-vehicle variables. Kanstrup and Lundin (2006) found correlations between driver sleepiness and two variables, reaction time and degree of interaction. These should be explored as well as other variables that are shown to be correlated with driver sleepiness. To be able to develop an algorithm relying on only in-vehicle variables, extensive experiments needs to be done to collect adequate information from real situations. This means that quantitative tests have to be performed in a simulator. Through these tests, proper variables could be identified that shows when a driver is sleepy. The best variables should be combined to create a formula that, if shown to be effective, might be implemented in future systems Purpose and goal The purpose of this master thesis project is to try out a way to prevent truck drivers from falling asleep behind the wheel, hence reducing the number of dead or injured in traffic and making the Scania truck a safer work environment for commercial drivers. The goal is to develop an algorithm that predicts when a driver is falling asleep or is about to fall asleep, independent of conditions like lighting, weather or curvature of the road. The algorithm should be applicable in a real truck and should not give false alarms that would be disturbing to the driver Limitations - The algorithm should be created for real-time usage in trucks, driving on motorways, highways and main roads with a speed over 65 km/h. The differences between driving behaviors depending on road types is not considered in this report. - This project will focus on sleepiness, even if other inattentive conditions like drug influence maybe could be measured in a similar way. - Legal and ethical issues will not be treated. - This project will not focus on if the driver is disturbed/suffers from an illness. - The tests will be conducted in a Swedish environment with Swedish roads and weather conditions. - Implementation of the algorithm into a system is not considered in this project. - The evaluation of driver sleepiness is done after the tests are performed and the project will discuss but not consider how the algorithm should be implemented in real time Difference from earlier work It exist in-vehicle systems today that claim to detect or predict driver sleepiness from one or several variables. However, no system is generally accepted since there are difficulties with all different methods. Also, the empirical proofs for the systems are missing or protected in most cases. According to Åkerstedt & Kecklund (2000), many research projects are ongoing at the moment in this area but no system has proved to give an accurate and safe warning system. Therefore, the difference from earlier work is the usage of only in-vehicle variables in the detection of driver sleepiness. 1.3 Previous thesis project This master thesis project is a continuation of Maria Lundin and Lena Kanstrup s master thesis project for Scania CV AB during autumn 2005 and spring They investigated if an existing patent for sleepiness detection, belonging to Cesium AB, could be useful in Scania s trucks. They performed two day-time and two night-time tests in a simulator to test the patent algorithm and also a validation test in a real truck to see if the results from the simulator were applicable to a real truck environment. In the case that they found the patent to be useful they should specify which variables that is best combined in an algorithm. (Kanstrup & Lundin, 2006) The patent belonged to Hans Eriksson at Cesium AB and describes a method for measuring the status of the driver s vehicle control. The purpose was to identify variables which could be used as indicators of the driver s 14

16 wakefulness. The patent method introduces the term micro communication which is considered to be the subconscious interaction between the truck and the driver, in a certain frequency range. Typically this micro communication is usually no more than 0.1% of the maximum steering wheel rotation of a private car. (Eriksson, 2005) The degree of interaction in this micro communication indicates if the driver is alert or not. This together with the other variables reaction time, answered question frequency and variation of amplitude are then combined into an algorithm that is said to be able to detect a sleepy driver. According to the method, the interaction between the driver and the truck can be measured through how well the truck s lateral acceleration corresponds with the torque that the driver puts on the steering wheel. This interaction is measured through different variables which is the combined into an algorithm. (Eriksson, 2005; Björkman, 2005) The different variables were evaluated and the conclusion made by the authors was that two of the four variables in the patent algorithm showed correlation with the sleepiness of the driver. This was the reaction time and degree of interaction and were therefore of interest for further studies. Kanstrup & Lundin suggested that further studies with more night-time simulator tests needed to be conducted to be able to continue developing an algorithm for sleepiness detection. 1.4 Division of work This master thesis project is as stated before a collaboration between Kristina Mattsson and Jens Berglund. The assignment, purpose and goal of the project are the same but the work will be partly divided. Two approaches is used, lane position variables and steering wheel variables, where this report will focus on the former one. The literature study is conducted in either area although the testing is combined. The analysis is then performed according to what variables that are looked upon. Table 1 shows a schematic view how the work is divided. Table 1 Division of work Literature review Kristina Mattsson Mainly lane position but also steering wheel variables. Jens Berglund Mainly steering wheel but also lane position variables. Simulator testing The testing in the simulator was performed by both. Analysis The lane position variables, reaction time and frequency components of the raw variables were analyzed. The steering wheel variables and degree of interaction were analyzed. Formula In the final formula the variables were combined to create the optimal result. 1.5 Outline of the report This report is divided into 8 main chapters, starting with an introduction to the background to the problem which leads to and the purpose, goal, limitations and the difference from earlier work. The final thesis project preceding this one is presented in subchapter 1.3 and in 1.4 a table shows how the collaborating authors divided the work between them. The theoretical frame of reference in chapter two contains the necessary theory later used in the project and the chapter following that presents the technical issues. In chapter four the method is described where extensive simulator experiments were conducted. Results from these experiments are shown in chapter five with following discussions in chapter six. A conclusion of the project finishes the report part followed by the references and appendixes. 15

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18 2 Theoretical frame of reference This chapter comprises information and theory needed for the realization of this project. Terms and definitions to define sleepiness, factors that could enhance the driver sleepiness and the measure used in this report to estimate sleepiness are presented in the first subchapter. The second and third subchapters present the different independent variables that were found in literature to indicate sleepiness. A method is needed to find out which of these variables that should be included in the final formula. Different statistical methods for this are evaluated in subchapter four. The theoretical frame of reference finishes of presenting a way to judge the performance of the final formula. 2.1 Sleepiness Driver sleepiness is generally associated with loss of vigilance and vehicle control. According to Dinges (1995), even moderately sleepy persons can contribute to severe traffic accidents since they show increased periods of non-responding or delayed responding. The underlying problem when trying to prevent sleepiness-related accidents is of course the sleepiness of the drivers. To understand the complexity of sleepiness is hard and no definite measure exists to tell if a person is sleepy or not. This subchapter will define the sleepiness terms used in this report Definitions Tiredness, fatigue, exhaustion, drowsiness, inattention, distraction and drug influence are all human conditions that have similar effect on the driving behavior. Some of these expressions describe a human condition that is caused by outer circumstances, such as drug-use or physical or mental work. This project aims to detect when a driver is about to fall asleep behind the steering wheel, not depending on outer circumstances. According to Åkerstedt (2000), the term sleepiness describes an inability to stay awake. Sleepiness is often used synonymously with drowsiness but the latter term has a wider meaning in the sense that the cause of the condition is not as defined. Drowsiness can be caused not only by exhaustion and lack of sleep but also by drug- 17

19 use. In this report, sleepiness will be used for the human condition ready or inclined to sleep. (Dictionary.com, 2007) Sleepiness factors There are different reasons why drivers become sleepy and fall asleep behind the steering wheel. Some are related to the cab and road environment, some to the condition the driver is in. Some of the issues addressed by authors in the literature review are presented in this section. Knowing what causes fatigue and sleepiness was important when planning and executing this project. Pack et al. (1995) noticed in a study that being awake for more than 20 hours drastically increases the risk of an accident. NTSB (1995) stated that the most important factors to 58 % of the fatigue related accidents were the length of the drivers last sleep, total amount of sleep the last 24 hours and fragmented sleeping patterns (several short sleep intervals). (Åkerstedt & Kecklund, 2000) Studies also show that the circadian rhythm has an effect on the level of sleepiness. Analysis of police-reports in both the USA and Europe shows a pattern where sleepy driving accidents elevates during night-time and mid-afternoon. (Dinges, 1995) Referenced in Åkerstedt & Kecklund (2000) is SCB/SIKA (1999) who shows Swedish statistics that 41 % of the accidents where only one vehicle was involved in Sweden 1998 happened in darkness. Åkerstedt and Kecklund (2000) performed their own studies but they saw no significant proof that darkness as a single factor made drivers fall asleep but rather that the circadian rhythm makes the body want to sleep at night-time. They looked at fatigue induced accidents on Swedish highways 7 hour by hour and noticed that most accidents occur at three or four in the morning during week-days, independent if is was summer or winter, hence indicating that the darkness was not the main reason for falling asleep. They also saw that during the week-ends the peak of accidents appeared later in the morning, around eight or nine, which indicates that it has more to do with lack of sleep than the lighting conditions. The results from tests where driving-time were measured as a possible factor for sleepiness are hard to separate from the effects of time of day or the amount of sleep that driver have had lately. Fell (1995) showed that 59 % of all fatigue-related accidents occurred within two hours from start, the average was between two and three hours from start. There are no convincing proofs that the length of driving time is essential concerning the risk of accidents. (Åkerstedt & Kecklund, 2000) Cab environment, gender and age of driver and time of year are other factors that might add to the risk of falling asleep. Also, the amount of noise in the cab environment could be a factor that increases the sleepiness of the driver. Löfstedt et al. (1988) showed that the level of performance goes down when the continuous average intensity noise goes up. The character of the road is another factor that has been mentioned by several authors, although no extensive studies have been made. (Åkerstedt & Kecklund, 2000) An obvious and important sleepiness risk factor is sleep disorders and Dinges (1995) recommends that this area should be addressed in a work/accident arena Sleepiness measure To be able to extract variables that indicate sleepiness when performing simulator tests, a reliable definitional measure of the driver s actual sleepiness is required. Such a measure may be based on different attributes such as physiological, performance or subjective measures. It need not be obtained operationally but it must be available in the experiments so that the final formula can be trained to indicate a sleepy driver. (Knipling & Wierwille, 1994) Sleepiness is possible to assess both objectively, with wiring and sensors, and subjectively, by for example Stanford Sleepiness Scale or Karolinska Sleepiness Scale. (Kanstrup & Lundin, 2006) All of the known techniques of measuring sleepiness are limited in some way. There is a trade-off between the difficulty to perform the measurements and the correlation between the actual sleepiness and the measure. This indicates the complexity of sleep and that it is individually changing. PERCLOS is a well-known objective measure that uses cameras and image processing to determine how large proportion of a time interval that the eyes of the driver are % closed (exclusive of blinks). This measure is generally accepted as a reliable way to measure fatigue but is hard to implement. Well-placed cameras with high resolution is needed as well as advanced image processing software. The driver can not move around too much in the cab and the use of glasses can complicate the measurement and make it impossible to assess data at 7 Swedish: Europavägar 18

20 all times. The Karolinska Sleepiness Scale 8 (KSS) is a subjective sleepiness measure that is much easier to use since the subject rates his/her own sleepiness on a scale. It has been validated in several studies, for example in a study by Kaida et al. (2006) where the KSS were compared to EEG and the behavior of the test subject. There are several methods for subjective assessment of a person s sleepiness. When using the KSS the person is asked about his/her tendency, intention or potential for falling asleep at that particular moment. (Sagberg et al., 2004) The KSS is a nine grade subjective scale where rate one indicates a very alert driver and nine a very sleepy one. The different steps are defined as in Table 2: Table 2 Karolinska Sleepiness Scale Rate State 1 extremely alert 2 very alert 3 alert 4 somewhat alert 5 neither alert nor sleepy 6 slightly sleepy 7 sleepy, but not strenuous to stay awake 8 sleepy, somewhat strenuous to stay awake 9 very sleepy, great effort to stay awake, fighting sleep According to Åkerstedt (2006) the scale is linear compared to a base scale, i.e. the distance between for example KSS value one and two is equal to the distance between KSS value eight and nine. The main focus for this project is to predict or at least detect when a person is an eight or a nine on the scale, this is when the sleepiness is becoming hazardous for continuous driving. Åkerstedt (2006) have made studies where it is shown that persons that rank themselves to be a nine are estimated to drift off the lane in as much as 17 % of the cases. The KSS is easy to use compared to objective measurements but there are also drawbacks with the method. According to Tijerina (1999) research shows that the problem with subjective sleepiness ratings is that the subject might not always be good at gauging how drowsy they are or when they are likely to fall asleep. The authors discusses in their report (page 3) that Drowsy driver detection algorithms and approaches have been a topic of considerable research in recent years. A key ingredient in the development of such algorithms is selection of and appropriate criterion measure for drowsiness. Such a measure of drowsiness should ideally be valid and reliable. This kind of discussion is found in several reports and the dilemma is finding a good way to measure sleepiness that is reliable and that could be relatively easy implemented. 2.2 Independent variables - lane position measures This chapter will describe different lane position variables that in the literature is shown to be correlated to driver sleepiness and that is later used in the formula. These variables will be called transformed variables in this report. To compute a transformed variable, one or more raw variables are used (they are further presented in chapter 4.1.3). The raw variables are the variables collected from the real vehicle using line tracking cameras, gyro meters and accelerometers. As mentioned in the last subchapter, PERCLOS is a well-known and accepted independent measure of sleepiness and is closely related to driving patterns and performance. When the eyes are closed due to sleepiness, visual inputs to the driver are temporarily halted. This means that the driver might hold the steering wheel still or almost still and the vehicle will continue in a straight path. During this period of driver inattention, variations in the road geometry and other disturbances as wind gusts may make the vehicle drift out of the lane. When (or if) the driver opens the eyelids again to assess the driving situation, a more or less sudden correction will be needed to keep the vehicle in the lane. (Tijerina, 1999) This will be shown in lateral acceleration of high amplitude or relatively large lane drift and can therefore be measured with in-vehicle variables. Measuring in-vehicle variables therefore show great potential for prediction of driver sleepiness. For example, Tijerina et al. (1999) 8 The KSS was developed by Professor Torbjörn Åkerstedt and his group in

21 finds that those measures related to steering inputs and lane-keeping appear to be the most promising indicators of sleepiness or impaired driving performance. Research shows that the lateral lane position of the vehicle contains a lot of information about the driver s vehicle control and therefore it is a promising measure for detection of sleepy drivers. The most common lane position measures are different lane drift variables and Time-to-Lane Crossing (TLC, presented in subchapter 2.2.4). There are different ways of measuring the lane drift, such as lane position variation, mean lateral position and vehicle path deviation. The lane drift measures are usually closely related since they are describing the same behavior but in different ways. The general opinion is that the vehicle control diminishes as the driver gets sleepier; hence the vehicle lateral position varies more. Åkerstedt & Kecklund (2000) found that tests in a truck simulator during night time involves an increase of variation in lateral position i.e. the truck wobbles over the lane more with a tired driver. Referenced in Tijerina et al. (1999) is Allen, Parseghian, and Stein (1996) who reported that as the standard deviation of the lane position increased beyond about 0.8 ft 9 (relative to lane centre), the probability of a lane departure goes up drastically. In an experiment in a simulator done by Sagberg et al. (2004) changes in the lateral deviation was measured when the driver drove non-stop for six hours. The lateral deviation started to increase considerably after the fourth hour of driving. This also showed a correlation with the driver sleepiness, measured with EEG, which implicated that the driver was becoming more and more sleepy. Some of the in-vehicle related measurers have been proven to show no signs of driver sleepiness and will not be used in this report. Wierwille et al. (2001) refers to Huntley and Centybear (1974) who states that brake usage did not significantly change with sleep deprivation. Also, speed and longitudinal acceleration was not shown to be correlated with sleep deprivation and fatigued driving. This is also the conclusion of Kircher et al. (2002) who says that the speed of the vehicle shows no correlation with the sleepiness of the driver. There are some problems with measuring only lane position variables. The idea of a sleepiness prediction or detection system where only in-vehicle variables are used is based on the fact that an experienced driver, subconscious and under normal conditions, handles the vehicle as an extension of the body. (Kanstrup & Lundin, 2006) This means that each driver will show an individual driving style which will cause problems in the automatic recognition of sleepiness through measurement of variables such as lateral position. (Kircher et al., 2002) There are actually stated cases where the driver takes small naps on long straight sections of the road. In this state he/she will hold the steering wheel still and will therefore drive straight if the lateral influences are small or moderate. Measuring only one road position variable will then give a misleading result since the thought is that the driver s vehicle control diminishes when the driver is getting sleepier. To solve the problem with adapting a general formula to drivers with different driving styles Knipling & Wierwille (1994) introduced the term baselining. They state (page 10) that A baselining procedure will be used to tailor detection formulas to the individual driver. It will record each driver s performance measures online initially and then subtract such values from all subsequent values. Accordingly, measures obtained are actually deviations from the driver s own baseline Theory - lateral position variance and standard deviation Maybe the most straight-forward lane drift measure is the standard deviation 10 and variance 11 of the lateral position, i.e. how much the vehicle wobbles over the lane. Following the discussion from previous subchapters a sleepy driver would be characterized by larger fluctuations from the lane path and this would show in an increase of standard deviation and variance of the lateral position as the driver is getting sleepier. The variance is calculated as the squared standard deviation and is therefore only a 2nd order scaling. Referenced in Kircher et al. (2002) are two studies that showed that the standard deviation of lateral position increased as the driver got sleepier. (Allen & O Hanlon, 1979; Stein et al., 1995) 9 About 25 centimeters. 10 Standard deviation is defined as the square root of the average of the squares of deviations about the mean of a set of data. More generally, a measure of the extent to which numbers are spread around their average. (interstorwords.com, 2007; childrens-mercy.org, 2007) 11 A measure of the average distance between each of a set of data points and their mean value; equal to the sum of the squares of the deviation from the mean value. (investorwords.com) 20

22 2.2.2 Theory - mean lateral position There are theories and studies that suggest that the mean position of the road could be used as an indicator of driver sleepiness. Driving on a motorway where the lanes are separated by fences or ditches makes the driver (subconsciously) keep to the left of the lane since a lane exceeding to the right probably would cause more damage. On the other hand, when there is on-coming traffic, tired drivers tend to keep to the right since a headon collision is more dangerous than drifting off the road. Within the SAVE-project 12 the mean of the lane position over a time interval was used to predict driver sleepiness. It showed that the driver kept further to the right of the lane with prolonged driving. This means that the driver, consciously or subconsciously, keeps to the part of the lane where the damage from an accident would be the least. They also studied the time preceding the vehicle drifted of the road. This showed that the average lateral position tended to move to the right, some time before drifting of the road Theory - vehicle path deviation Following the discussion of detection of sleepiness from diminished vehicle control, another driving behavior measure could be to assess the area of vehicle deviation. Computing the vehicle mean lateral position over a time interval would enable the measurement of the deviation from this path. The area would increase when the driver becomes more and more tired. An example illustration of this is shown in Figure 1. Mean lateral position Vehicle Lateral position Path deviation area Figure 1 Vehicle path deviation Theory - Time-to-Lane Crossing Time to Lane Crossing (TLC) was first proposed by Godthelp in 1984 and is by definition the time available until any part of the vehicle reaches one of the lane boundaries, following the trajectory given by the present vehicle direction and velocity. (Batavia, 1999) According to Kircher et al. (2002), TLC can help prevent diminished performance and warn the driver before the vehicle actually drifts of the lane. This is also supported by van Winsum et al. (1999) who states that accidents where the driver inadvertently moves of the road often are preceded by a period during which the TLC minimum 13 is low. This suggests that lane control of the vehicle 12 System for effective Assessment of the driver state and Vehicle control in Emergency situations. A European traffic safety program. 13 A TLC minimum is when the vehicle is close to crossing the lane boundary but the driver avoids the lane exceeding by a steering correction. A TLC minimum will therefore not occur prior to a lane exceeding. 21

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